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98a6f9a0b1
The initial M-series specs were wrong (text digestion pipeline). Replaced
with the actual economy organ architecture:
M0 hub (independent system, scoped autonomy, multi-layered braking)
M1 Marketplace (multi-trader harness, deterministic law script, veto)
M2 Data Feeds (RSS + live market, bridges Marketplace ↔ Sims)
M3 Sims hub + 7 sub-specs (always-running, bounded predictions):
M3a statistical, M3b sociological, M3c AMM/liquidity,
M3d MEV/adversarial, M3e tokenomics/macro, M3f consensus/staking,
M3g market microstructure
M4 Wallets (sovereign custody, our keys only, 1:1 trader binding)
M5 Traders (AI actors, wallet-bound, all tool calls monitored)
M6 Conductor (supervisory AI, veto, pause/investigate, SAE intake)
M7 SAE monitor (trader surveillance, Brain-compatible message format)
Grounded in AMM invariant mechanics, MEV game theory, SDE tokenomics,
and evolutionary consensus games. Tax stub for Verschwörern Veregeister.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
76 lines
4.3 KiB
Markdown
76 lines
4.3 KiB
Markdown
# M3 — Sims hub (market prediction simulations)
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## 1. Component
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The economy organ's prediction engine: **always-running simulations** ("Sims") populated by
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autonomous simulation agents ("Pops") that model market dynamics across multiple mathematical
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domains and time scales. Sims are **queryable at any time** by Traders (M5) — they produce
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**predictions with explicit upper and lower bounds** on every output value. This is the hub spec;
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individual sim types have dedicated sub-specs (M3a–M3g).
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The academic foundations span AMM mechanism design [1,2], MEV game theory [3,4,5], macro
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tokenomics via SDEs [6,7], and evolutionary consensus games [8–11].
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## 2. Status / certainty
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DESIGN-FIRST · ABSENT. Role C3; implementation C1. Mathematical foundations C4 (literature
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established); specific model parameters C1.
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## 3. Language & location
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TBD · `src/economy/sims/`. Numerical computing (Julia, Python/NumPy, Octave, or Rust) for the
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simulation cores. A query facade accessible to Traders. Each sim type (M3a–M3g) may use a
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different runtime suited to its math.
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## 4. Does / does-not
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- **Does:** run continuously across multiple time scales (tick-level, hourly, daily, weekly);
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maintain populations of Pops whose behaviors emerge from the sim's mathematical model; ingest
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live data from Data Feeds (M2) for calibration; respond to Trader queries with bounded
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predictions; produce outputs with **explicit upper/lower bounds** on every prediction value.
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- **Does-not:** trade (Traders/Marketplace do); make decisions for traders (it informs, they
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decide); enforce laws (Marketplace does); supervise behavior (Conductor/SAE do).
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## 5. Interface contract
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- `query(sim_type: SimType, query: PredictionQuery) -> BoundedPrediction`.
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`SimType` ∈ { `statistical`, `sociological`, `amm_liquidity`, `mev_adversarial`,
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`tokenomics_macro`, `consensus_staking`, `market_microstructure` } (M3a–M3g).
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- `BoundedPrediction { value, lower_bound, upper_bound, confidence, time_horizon, sim_type, timestamp }`.
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Every output is bounded — no point estimates without uncertainty ranges.
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Example: `{ value: 7.2, lower_bound: 5.8, upper_bound: 8.9, confidence: 0.73,
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time_horizon: "4h", sim_type: "amm_liquidity" }`.
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- `status(sim_type?) -> { running, pop_count, last_calibration, data_freshness }` — health check.
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- `calibrate(sim_type, feed_data: [NormalizedDatum])` — Data Feeds (M2) pushes live data for
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model recalibration.
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## 6. Dependencies & stubs
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- M2 Data Feeds — calibration data source; *stub:* canned market data.
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- M5 Traders — query consumers; *stub:* canned queries.
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- M3a–M3g sub-specs — individual sim implementations; *stub:* each returns fixed predictions.
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## 7. Invariants / laws
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- **L1 (C5):** sims are **always running** — they are not invoked on demand. Traders query
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current state; they don't trigger computation.
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- **L2 (C5):** every prediction output includes **explicit upper and lower bounds** — no
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unbounded point estimates. Uncertainty is a first-class value, not an afterthought.
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- **L3 (C4):** sims are **read-only from traders' perspective** — a query never mutates sim
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state. Calibration happens only from Data Feeds (M2).
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- **L4 (C4):** each sim type is **independent** — failure in one sim does not cascade to others.
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Degraded sims report their status; traders handle missing predictions.
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- **L5 (C3):** Pops are **simulation constructs, not AI actors** — they follow mathematical
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rules within the sim. Traders (M5) are the AI actors.
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## 8. Build steps
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1. Define `BoundedPrediction` shape and query protocol.
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2. Build the sim runner (lifecycle management for always-on sims).
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3. Wire M2 Data Feeds → calibration pipeline.
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4. Implement sub-specs M3a–M3g as they land.
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5. Wire trader query interface.
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## 9. Tests
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Always-on: sim running after init without external trigger. Bounded output: every prediction has
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lower ≤ value ≤ upper. Query: trader receives prediction without mutating sim. Independence:
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one sim's failure doesn't affect others. Calibration: new data updates model state.
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## 10. Open items
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- Pop lifecycle (birth/death/mutation within sims, or fixed populations?).
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- Cross-sim aggregation (do traders query individual sims, or is there a meta-prediction layer?).
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- Calibration frequency per sim type.
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- Computational budget per sim (how much CPU/GPU each can consume).
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